We discussed a few important concepts primarily related to 2D DFT and its related applications in image processing, such as filtering in the frequency domain, and we worked on quite a few examples using scikit-image numpy.fft, scipy.fftpack, signal, and ndimage modules.
Hopefully, you are now clear on sampling and quantization, the two important image formation techniques. We have seen 2D DFT, Python implementations of FFT algorithms, and applications such as image denoising and restoration, correlation and convolution of the DFT in image processing, and application of convolution with an appropriate kernel in filter design and the application of correlation in template matching.
You should now be able to write Python code to do ...
